Ring-flash-2.0

Ring-flash-2.0 is a high-performance thinking model, deeply optimized based on Ling-flash-2.0-base. It is a Mixture-of-Experts (MoE) model with a total of 100B parameters, but only 6.1B are activated per inference. The model leverages the independently developed 'icepop' algorithm to address the training instability challenges in reinforcement learning (RL) for MoE LLMs, enabling continuous improvement of its complex reasoning capabilities throughout extended RL training cycles. Ring-flash-2.0 demonstrates significant breakthroughs across challenging benchmarks, including math competitions, code generation, and logical reasoning. Its performance surpasses that of SOTA dense models under 40B parameters and rivals larger open-weight MoE models and closed-source high-performance thinking model APIs. More surprisingly, although Ring-flash-2.0 is primarily designed for complex reasoning, it also shows strong capabilities in creative writing. Thanks to its efficient architecture, it achieves high-speed inference, significantly reducing inference costs for thinking models in high-concurrency scenarios